Papers by Martin Volk
Evaluation of HTR models without Ground Truth Material (2022.lrec-1)
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Phillip Benjamin Ströbel, Martin Volk, Simon Clematide, Raphael Schwitter, Tobias Hodel, David Schoch
| Challenge: | Optical Character Recognition (OCR) is a well-established technique for digitising historical printed collections in libraries and archives. |
| Approach: | They propose to use masked language models to evaluate handwritten text recognition models . they propose to introduce GT-free metrics to evaluate models to ensure best results . |
| Outcome: | The proposed model evaluations are based on lexicon-based and masked language models. |
Nunc profana tractemus. Detecting Code-Switching in a Large Corpus of 16th Century Letters (2022.lrec-1)
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Martin Volk, Lukas Fischer, Patricia Scheurer, Bernard Silvan Schroffenegger, Raphael Schwitter, Phillip Ströbel, Benjamin Suter
| Challenge: | a corpus of 16th century letters from and to the Zurich reformer Heinrich Bullinger has been preserved . a recent study investigated code-switching in these 8600 letters . |
| Approach: | They investigate the automatic detection of code-switching in a 16th century letter exchange . they use a popular language identifier to bootstrap a word-based language classifier . |
| Outcome: | The proposed language classifier bootstraps with a popular identifier on a small training corpus of 150 sentences per language. |
How Much Data Do You Need? About the Creation of a Ground Truth for Black Letter and the Effectiveness of Neural OCR (2020.lrec-1)
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| Challenge: | Recent advances in Optical Character Recognition and Handwritten Text Recognition have led to more accurate text recognition of historical documents. |
| Approach: | They propose to build a ground truth for a German-language newspaper published in black letter . they also evaluate the performance of different OCR engines and estimate how much data is needed to achieve high-quality OCR results. |
| Outcome: | The proposed model can recognise black letter text and performs well on data they have not seen during training. |
Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation (D18-1)
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| Challenge: | Recent research suggests that neural machine translation achieves parity with professional human translation on the WMT Chinese–English news translation task. |
| Approach: | They empirically test neural machine translation on a Chinese–English news translation task . they show human raters prefer human over machine translation when evaluating documents . |
| Outcome: | The proposed method shows that human translators prefer document-level evaluation over machine translation . the results highlight the need to shift towards document- level evaluation as machine translation improves . |